Jan Nedermeijer
Bob Uttl, Carmela A. White, Alain Morin 
Published: December 16, 2013. Affiliation Department of Psychology, Mount Royal University, Calgary, Alberta, Canada.
Undergraduate Students' interest in taking quantitative vs. non quantitative courses has received limited attention even though it has important consequences for higher education. Previous studies have collected course interest ratings at the end of the courses as part of student evaluation of teaching (SET) ratings, which may confound prior interest in taking these courses with students' actual experience in taking them.
This study is the first to examine undergraduate students' interest in quantitative vs. non quantitative courses in their first year of studies before they have taken any quantitative courses. Three hundred and forty students were presented with descriptions of 44 psychology courses and asked to rate their interest in taking each course. Student interest in taking quantitative vs non quantitative courses was very low; the mean interest in statistics courses was nearly 6 SDs below the mean interest in non quantitative courses. Moreover, women were less interested in taking quantitative courses than men.
Our findings have several far-reaching implications.
- First, evaluating professors teaching quantitative vs. non quantitative courses against the same SET standard may be inappropriate.
- Second, if the same SET standard is used for the evaluation of faculty teaching quantitative vs. non quantitative courses, faculty are likely to teach to SETs rather than focus on student learning.
- Third, universities interested primarily in student satisfaction may want to expunge quantitative courses from their curricula. In contrast, universities interested in student learning may want to abandon SETs as a primary measure of faculty teaching effectiveness.
- Fourth, undergraduate students who are not interested in taking quantitative courses are unlikely to pursue graduate studies in quantitative psychology and unlikely to be able to competently analyze data independently.